
'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Optional[int]   =  AutoencoderKL
	snake_case__   :int   =  'sample'
	snake_case__   :str   =  1e-2





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				4
				lowerCAmelCase__										=				3
				lowerCAmelCase__										=				(32, 32)

				lowerCAmelCase__										=				floats_tensor((batch_size, num_channels) + sizes				).to(__magic_name__				)

				return {"sample": image}





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return (3, 32, 32)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return (3, 32, 32)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				{
				    "block_out_channels": [32, 64],
				    "in_channels": 3,
				    "out_channels": 3,
				    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
				    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "latent_channels": 4,
				}
				lowerCAmelCase__										=				self.dummy_input
				return init_dict, inputs_dict





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				pass





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				pass





	@unittest.skipIf(torch_device == "mps"   ,				"Gradient checkpointing skipped on MPS"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.prepare_init_args_and_inputs_for_common()
				lowerCAmelCase__										=				self.model_class(**__magic_name__				)
				model.to(__magic_name__				)

				assert not model.is_gradient_checkpointing and model.training

				lowerCAmelCase__										=				model(**__magic_name__				).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model.zero_grad()

				lowerCAmelCase__										=				torch.randn_like(__magic_name__				)
				lowerCAmelCase__										=				(out - labels).mean()
				loss.backward()

				# re-instantiate the model now enabling gradient checkpointing
				lowerCAmelCase__										=				self.model_class(**__magic_name__				)
				# clone model
				model_a.load_state_dict(model.state_dict()				)
				model_a.to(__magic_name__				)
				model_a.enable_gradient_checkpointing()

				assert model_a.is_gradient_checkpointing and model_a.training

				lowerCAmelCase__										=				model_a(**__magic_name__				).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model_a.zero_grad()
				lowerCAmelCase__										=				(out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1E-5				)
				lowerCAmelCase__										=				dict(model.named_parameters()				)
				lowerCAmelCase__										=				dict(model_a.named_parameters()				)
				for name, param in named_params.items():
							self.assertTrue(torch_all_close(param.grad.data   ,				named_params_a[name].grad.data   ,				atol=5E-5				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"   ,				output_loading_info=__magic_name__				)
				self.assertIsNotNone(__magic_name__				)
				self.assertEqual(len(loading_info["missing_keys"]				)   ,				0				)

				model.to(__magic_name__				)
				lowerCAmelCase__										=				model(**self.dummy_input				)

				assert image is not None, "Make sure output is not None"







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"				)
				lowerCAmelCase__										=				model.to(__magic_name__				)
				model.eval()

				if torch_device == "mps":
							lowerCAmelCase__										=				torch.manual_seed(0				)
				else:
							lowerCAmelCase__										=				torch.Generator(device=__magic_name__				).manual_seed(0				)

				lowerCAmelCase__										=				torch.randn(
				    1   ,				model.config.in_channels   ,				model.config.sample_size   ,				model.config.sample_size   ,				generator=torch.manual_seed(0				)   ,				)
				lowerCAmelCase__										=				image.to(__magic_name__				)
				with torch.no_grad():
							lowerCAmelCase__										=				model(__magic_name__   ,				sample_posterior=__magic_name__   ,				generator=__magic_name__				).sample

				lowerCAmelCase__										=				output[0, -1, -3:, -3:].flatten().cpu()

				# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
				# the expected output slices are not the same for CPU and GPU.
				if torch_device == "mps":
							lowerCAmelCase__										=				torch.tensor(
							    [
							        -4.0078E-01,
							        -3.8323E-04,
							        -1.2681E-01,
							        -1.1462E-01,
							        2.0095E-01,
							        1.0893E-01,
							        -8.8247E-02,
							        -3.0361E-01,
							        -9.8644E-03,
							    ]				)
				elif torch_device == "cpu":
							lowerCAmelCase__										=				torch.tensor(
							    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
				else:
							lowerCAmelCase__										=				torch.tensor(
							    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

				self.assertTrue(torch_all_close(__magic_name__   ,				__magic_name__   ,				rtol=1E-2				)				)






@slow
class  A  (						unittest.TestCase     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Any				):


				"""simple docstring"""

				return f"""gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__				) for s in shape]				)}.npy"""





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[str]=0   ,				__magic_name__      :							str=(4, 3, 512, 512)   ,				__magic_name__      :							str=False				):


				"""simple docstring"""

				lowerCAmelCase__										=				torch.floataa if fpaa else torch.floataa
				lowerCAmelCase__										=				torch.from_numpy(load_hf_numpy(self.get_file_format(__magic_name__   ,				__magic_name__				)				)				).to(__magic_name__				).to(__magic_name__				)
				return image





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							List[str]="CompVis/stable-diffusion-v1-4"   ,				__magic_name__      :							Optional[Any]=False				):


				"""simple docstring"""

				lowerCAmelCase__										=				"fp16" if fpaa else None
				lowerCAmelCase__										=				torch.floataa if fpaa else torch.floataa

				lowerCAmelCase__										=				AutoencoderKL.from_pretrained(
				    __magic_name__   ,				subfolder="vae"   ,				torch_dtype=__magic_name__   ,				revision=__magic_name__   ,				)
				model.to(__magic_name__				).eval()

				return model





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Union[str, Any]=0				):


				"""simple docstring"""

				if torch_device == "mps":
							return torch.manual_seed(__magic_name__				)
				return torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
	        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
	        # fmt: on
	    ]				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model()
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__				)
				lowerCAmelCase__										=				self.get_generator(__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model(__magic_name__   ,				generator=__magic_name__   ,				sample_posterior=__magic_name__				).sample

				assert sample.shape == image.shape

				lowerCAmelCase__										=				sample[-1, -2:, -2:, :2].flatten().float().cpu()
				lowerCAmelCase__										=				torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice				)

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=3E-3				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
	        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
	        # fmt: on
	    ]				)
	@require_torch_gpu
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model(fpaa=__magic_name__				)
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__   ,				fpaa=__magic_name__				)
				lowerCAmelCase__										=				self.get_generator(__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model(__magic_name__   ,				generator=__magic_name__   ,				sample_posterior=__magic_name__				).sample

				assert sample.shape == image.shape

				lowerCAmelCase__										=				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				lowerCAmelCase__										=				torch.tensor(__magic_name__				)

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=1E-2				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
	        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
	        # fmt: on
	    ]				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Union[str, Any]   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model()
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model(__magic_name__				).sample

				assert sample.shape == image.shape

				lowerCAmelCase__										=				sample[-1, -2:, -2:, :2].flatten().float().cpu()
				lowerCAmelCase__										=				torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice				)

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=3E-3				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
	        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
	        # fmt: on
	    ]				)
	@require_torch_gpu
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model()
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__   ,				shape=(3, 4, 64, 64)				)

				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				assert list(sample.shape				) == [3, 3, 512, 512]

				lowerCAmelCase__										=				sample[-1, -2:, :2, -2:].flatten().cpu()
				lowerCAmelCase__										=				torch.tensor(__magic_name__				)

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=1E-3				)





	@parameterized.expand(
	    [
	        # fmt: off
	        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
	        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
	        # fmt: on
	    ]				)
	@require_torch_gpu
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model(fpaa=__magic_name__				)
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__   ,				shape=(3, 4, 64, 64)   ,				fpaa=__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				assert list(sample.shape				) == [3, 3, 512, 512]

				lowerCAmelCase__										=				sample[-1, -2:, :2, -2:].flatten().float().cpu()
				lowerCAmelCase__										=				torch.tensor(__magic_name__				)

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=5E-3				)





	@parameterized.expand([(13,), (16,), (27,)]				)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()   ,				reason="xformers is not required when using PyTorch 2.0."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model(fpaa=__magic_name__				)
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__   ,				shape=(3, 4, 64, 64)   ,				fpaa=__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				assert list(sample.shape				) == [3, 3, 512, 512]

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=1E-1				)





	@parameterized.expand([(13,), (16,), (37,)]				)
	@require_torch_gpu
	@unittest.skipIf(not is_xformers_available()   ,				reason="xformers is not required when using PyTorch 2.0."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model()
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__   ,				shape=(3, 4, 64, 64)				)

				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
							lowerCAmelCase__										=				model.decode(__magic_name__				).sample

				assert list(sample.shape				) == [3, 3, 512, 512]

				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=1E-2				)







	@parameterized.expand(
	    [
	        # fmt: off
	        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
	        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
	        # fmt: on
	    ]				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.get_sd_vae_model()
				lowerCAmelCase__										=				self.get_sd_image(__magic_name__				)
				lowerCAmelCase__										=				self.get_generator(__magic_name__				)

				with torch.no_grad():
							lowerCAmelCase__										=				model.encode(__magic_name__				).latent_dist
							lowerCAmelCase__										=				dist.sample(generator=__magic_name__				)

				assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

				lowerCAmelCase__										=				sample[0, -1, -3:, -3:].flatten().cpu()
				lowerCAmelCase__										=				torch.tensor(__magic_name__				)

				lowerCAmelCase__										=				3E-3 if torch_device != "mps" else 1E-2
				assert torch_all_close(__magic_name__   ,				__magic_name__   ,				atol=__magic_name__				)










import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






